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Modality-aware Transformer for Financial Time series Forecasting
In practice, the key challenge lies in constructing a reliable time series forecasting model capable of harnessing data from diverse sources and extracting valuable insights to predict the target time series accurately.
AutoAI-TS: AutoAI for Time Series Forecasting
We present AutoAI for Time Series Forecasting (AutoAI-TS) that provides users with a zero configuration (zero-conf ) system to efficiently train, optimize and choose best forecasting model among various classes of models for the given dataset.
"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering
In this work, we address the problem of given a numerical time series, and a general corpus of textual stories collected in the same period of the time series, the task is to timely discover a succinct set of textual stories associated with that time series.
seq2graph: Discovering Dynamic Dependencies from Multivariate Time Series with Multi-level Attention
We introduce a key component of Dual-purpose recurrent neural network that decodes information in the temporal domain to discover lagged dependencies within each time series, and encodes them into a set of vectors which, collected from all component time series, form the informative inputs to discover inter-dependencies.
